Videos

Metis can visualize your schemas, find changes, and show how indexes evolved over time.

Metis can visualize your MySQL queries with an easy-to-read plan visualization. You can process the JSON-based execution plan and easily see what it represents.

Metis can recommend indexes based on the actual query usage. We can get accurate measurements showing how new indexes will change the performance of the database queries.

Metis compares your hosts and shows a clear overview of all the changes: queries, tables, indexes, technical specifications, and databases. This gives you an understanding of how two environments differ.

Alert Context brings contextual information to your alerts to make the troubleshooting easier. You don't need to dig into metrics anymore to understand what happened.

Schema visualization lets you see all your tables, columns, and data types inside your database. This video shows how to access this feature.

Let's see how to configure custom rules for monitoring our databases to get automated alerts when metrics cross the thresholds.

Let's see how to integrate Metis with external platforms like Slack or Jira to deliver alerts.

In this video, we show how to integrate Metis with your CI/CD pipelines to get automated checks for your performance, queries, and schema migrations. Metis can integrate with any CI/CD pipelines of your choice. Here we present GitHub Actions integration.

Metis captures your database activiy and configuration, and provides actionable insights about performance, extensions, schemas, and settings to improve performance.

Metis Metadata Collector (MMC) is a component that connects to your database and extracts data for the dashboard.

See how to tune queries with Sequelize and Metis.

Tables Summary view shows all tables involved in the query execution. We can use this view to quickly understand access patterns, see indexes, number of extracted rows, or how accurate our filters are.

Metis Metadata Collector (MMC) Dashboard is a component in Metis that shows the observability dashboard, charts, metrics, and all the monitoring of your database server. MMC Dashboard is a must-have tool for every production database.

Execution Plan tab shows us the raw execution plan extracted from the database. We can use this screen to get the plan and use it with other tools of our choice.

Query can be very complex and consist of multiple layers. The easiest way to understand how it works is to see it. Query Tale tab of the Performance Insights can visualize the query and show all operations it performed.

Performance Insights is crucial for understanding how the query will perform in production. In this video we explore insights, remediation plans, how to access the automated analysis performed by Metis, and how to reason whether the performance should be improved.

When we observe issues with a query, the first thing to check is what is the query text. With SQL tab of the Performance Insights we can see the query with parameters, see which tables it tries to access, and what was generated by the ORM.

Main Project Page is an entry point for all of the functionality for performance tracking. In this video we explore features it has and how it works.

In this video we see how to integrate our JavaScript (or TypeScript) based application with Metis. We cover main flow, end-to-end tests, and connecting all of the pieces together.

We can integrate Metis with our CI/CD pipeline to automatically analyze schema migrations. We can use that to automatically find issues with tables, columns, indexes, partitions, and other DDL queries that we trigger when deploying the application. Featured playlist

We can integrate Metis with our CI/CD pipeline to automatically capture performance traces for every pull request we create. We can use that to automatically analyze performance of the code modified in a given pull request. This way we can find the performance degradation before going to production.

We can integrate Metis with our CI/CD pipeline to automatically capture performance traces every time we push a code to repository. We can use that to automatically analyze performance before going to production or even to staging environment.

Query Analyzer module is a tool for ad-hoc analysis of your queries. Just insert your SQL query + execution plan, and you get performance insights. Metis will analyze tables, indexes, functions, filters, I/O, buffers, and other performance metrics. You'll also get instruction how to fix the issues.

Like there are so many application tools available, Metis believes that database monitoring and observability fits the new era where developers are accountable for everything related to their database. From pre-deployment validation, monitoring, to the final troubleshooting. We believe developers deserve better tools to perform their database related activities.

We don’t need to run the query to see how it’s going to perform. We can ask the database to tell us exactly what it’s going to do. We can capture these details with OpenTelemetry and combine them with signals from our HTTP layer, ORM library, and database driver. Finally, we can analyze these with machine learning algorithms, to automatically troubleshoot issues, identify improvements, and submit corrections.

Most platform engineers are scared of databases, and most database administrators prefer it that way. But our guest today says it’s time to move forward together toward a future of database linters, observability, and abstraction. Adam Furmanek makes the case that just like how developers had to learn some networking, and networking tools were created to meet their needs, now the same must be done with databases. He walks us through database history, why DevOps should care about databases more now than ever before, and why he predicts a future full of DevDBOps… or something like that.

We went through a big transformation in recent decades, we learned how to deliver the software better, and we devised new practices and patterns to streamline the work between the teams. However, the current software development lifecycle (SDLC) is still far from being perfect. Teams spend much time on the handoffs of the artifacts. They communicate inefficiently, and they lack proper checks early in the pipeline. Platform Engineers can reshape the industry by pushing left guardrails to protect production, however, they need to have proper tools and processes.

Don’t wait for load tests to complete. Use OpenTelemetry to capture database signals, run them through cutting-edge machine learning solutions, and troubleshoot your database issues automatically! We don’t need to run the query to see how it’s going to perform. We can ask the database to tell us exactly what it’s going to do. We can capture these details with OpenTelemetry and combine them with signals from our HTTP layer, ORM library, and database driver. Finally, we can analyze these with machine learning algorithms, to automatically troubleshoot issues, identify improvements, and submit corrections. In this talk we are going to see how to use OpenTelemetry with modern databases to reason about the performance and correctness of our applications. We are going to use expert knowledge supported by AI to fix problems automatically. All of that for free with modern tooling.

In this episode, James learns from Roee how you can transition from engineering to the lofty heights of the C-Suite level, where Roee now sits. Roee also shares how he left a larger organisation to help co-found his new vision in Metis. Not only that, but Roee explores the kinds of customers who support his business and where Metis plans to go in the next 18 months. Lots of exciting things so far and much more in the pipeline for this business.

Designing SDKs is much different than designing business applications. Let’s see how to build software that works for years and is used by other developers. We’ll learn how design patterns work in real life, how mistakes made in the early stage affect the software years later, and how to refactor the library without breaking users.

You can’t deploy blindly and let your application fail. You need to prevent the bad code from reaching production, monitor your databases, and automatically troubleshoot when something is wrong. Let’s see how to do that in the SQL world.

Today’s industry standard for developing and maintaining databases is less than ideal. We don’t have a good way of preventing the bad code from reaching production. Once we deploy, we lack good tooling for observability and monitoring. Furthermore, we don’t have a way of troubleshooting and resolving issues reliably and automatically. Developers often don’t know how to proceed with their databases, and also often don’t own the solutions. We need something new. We need a paradigm shift and a completely novel approach: database guardrails.

Today's topic is devops challenges when scaling products and companies. Listen to learn what can be surprising when the product becomes bigger, how organizations need to change as they grow, and how to tackle communication when the company grows.

You can’t deploy blindly and let your application fail. You need to prevent the bad code from reaching production, monitor your databases, and automatically troubleshoot when something is wrong. You need database guardrails, and you need them now.

You can’t deploy blindly and let your application fail. You need to prevent the bad code from reaching production, monitor your databases, and automatically troubleshoot when something is wrong. You need database guardrails, and you need them now.

Today's topic is Extensions in PostgreSQL. Listen to learn why they are useful, how to use them, and which extensions are a must-have for our databases.

In this episode, we talk to Adam Furmanek (Head of DevRel at Metis) about what database developers can do to ensure their applications work and scale. Adam discusses monitoring, observability, and things to consider pre & post production.

CI/CD is common nowadays. We have tools and processes for reviewing our code, testing it, and deploying to production. What we miss is a well-crafted solution for automated database review. Let’s see how to do database review automatically, how to integrate them with GitHub pipeline, and how to make sure we don’t break the production database with inefficient queries.

One of the most common issues that cause production outages is the database, poor schema migrations, resource bottlenecks, and just plain misconfiguration can bring you to your knees. In the episode we explore the world of database observability. https://www.metisdata.io/

An interview with Adam Furmanek from Metis, about developers' relation to databases and a demo of Metis.

CI/CD is common nowadays. We have tools and processes for reviewing our code, testing it, and deploying to production. What we miss is a well-crafted solution for automated database review. Let’s see how to do database review automatically, how to integrate them with GitHub pipeline, and how to make sure we don’t break the production database with inefficient queries.

CI/CD is common nowadays. We have tools and processes for reviewing our code, testing it, and deploying to production. What we miss is a well-crafted solution for automated database review. Let’s see how to do database review automatically, how to integrate them with GitHub pipeline, and how to make sure we don’t break the production database with inefficient queries.

CI/CD is common nowadays. We have tools and processes for reviewing our code, testing it, and deploying to production. What we miss is a well-crafted solution for automated database review. Let’s see how to do database review automatically, how to integrate them with GitHub pipeline, and how to make sure we don’t break the production database with inefficient queries.

CI/CD is common nowadays. We have tools and processes for reviewing our code, testing it, and deploying to production. What we miss is a well-crafted solution for automated database review. Let’s see how to do database review automatically, how to integrate them with GitHub pipeline, and how to make sure we don’t break the production database with inefficient queries.

Our SQL queries typically work well when we run them locally. There is no load, no concurrent transactions, not much data in the database. Problems pop up when we deploy to production and we realize the queries are just too slow. Let’s make sure this doesn’t happen and we can identify problems before deploying the code.

Let's see how to build modern databases

CI/CD is now a well-known part of software development. We have tools for linting, unit testing, UI testing, database schema migration, infrastructure as code, and many others. We review our code and make sure it is safe to deploy to production. However, what about SQL queries? Do we check them on a regular basis? Do we make sure they will work in a production environment?